Abstract: Agriculture is the backbone of India’s economy, and sugarcane is one of the most crucial commercial crops. However, manual identification of sugarcane leaf diseases often leads to delayed treatment and reduced yield. This paper presents an AI-based chatbot system for early detection and management of sugarcane diseases. The system integrates a machine learning model built using the Random Forest Classifier, trained on sugarcane leaf image data. The chatbot is developed using Flask and provides users with disease identification, treatment guidance, and multilingual voice/text interaction. The proposed model achieved accurate classification performance and enables farmers to receive real-time diagnosis and recommendations in their preferred language, improving productivity and sustainability in agriculture.

Keywords: Sugarcane Disease Detection, Artificial Intelligence, Chatbot, Random Forest, Flask, Agriculture Automation.

Downloads: | DOI: 10.17148/IARJSET.2026.13115